{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "sys.path.append('../')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building batches for training neural nets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome! This is the third tutorial of the [series](https://github.com/analysiscenter/radio/blob/master/tutorials), covering lung cancer research using RadIO. In this tutorial you'll learn about `sample_nodules`-action, that allows to **sample interesting parts of scans** along with cancerous masks, thus considerably augmenting the dataset for a segmenting net. You will also take a look at `Pipelines`-submodule, which contains implementations of several ready-made workflows for preprocessing. If you haven't read the first two tutorials, we encourage you to do that before tackling this one. Anyways, here is a quick reminder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Not-so-quick reminder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using RadIO, the first step is always to set up a `Dataset` - a structure (`instance` of a `Dataset`-class), indexing set of voluminous ct-scans on disk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from radio.batchflow import FilesIndex, Dataset\n",
    "from radio import CTImagesMaskedBatch\n",
    "\n",
    "LUNA_MASK = '/data/MRT/luna/s*/*.mhd'                                      # set glob-mask for scans from Luna-dataset here\n",
    "luna_index = FilesIndex(path=LUNA_MASK, no_ext=True)                       # preparing indexing structure\n",
    "luna_dataset = Dataset(index=luna_index, batch_class=CTImagesMaskedBatch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine learning-solutions always start with preprocessing. RadIO thinks of preprocessing as of chained sequence of actions -  a `Pipeline`. Each `Pipeline` represents a *plan* of what is going to happen with data, rather than a real computation, and is made of actions, implemented in RadIO ([or by you](https://analysiscenter.github.io/lung_cancer/intro/preprocessing.html#writing-your-own-actions)). E.g., you can set up a simple preprocessing pipeline, including `load` from [Luna dataset](https://luna16.grand-challenge.org/)-format and `resize` to shape **[92, 256, 256]** in a following way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from radio.batchflow import Pipeline                 # the cell executes fast\n",
    "simple_preproc = (Pipeline()                       # we only write a plan\n",
    "                  .load(fmt='raw')                 # no computations here\n",
    "                  .resize(shape=(92, 256, 256)))   # it happens later"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "It might be a good idea to replace `resize` with `unify_spacing`, that not only changes shape of scans, but also zooms them to the same scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "other_simple_preproc = (Pipeline()\n",
    "                        .load(fmt='raw')\n",
    "                        .unify_spacing(shape=(92, 256, 256), spacing=(3.5, 1.0, 1.0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also add some data-augmenting actions to your pipeline. E.g., `rotate` of scans or cropping out its central part using `central_crop`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "augmenting_pipeline = (Pipeline()\n",
    "                       .load(fmt='raw')\n",
    "                       .unify_spacing(shape=(92, 256, 256), spacing=(3.5, 1.0, 1.0))\n",
    "                       .rotate(angle=30)\n",
    "                       .central_crop(crop_size=(64, 192, 192)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also get your hands on target for a segmenting net - *cancerous masks*, by adding `fetch_nodules_info` and `create_mask` to a preprocessing pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriesuid</th>\n",
       "      <th>coordX</th>\n",
       "      <th>coordY</th>\n",
       "      <th>coordZ</th>\n",
       "      <th>diameter_mm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222...</td>\n",
       "      <td>-128.699421</td>\n",
       "      <td>-175.319272</td>\n",
       "      <td>-298.387506</td>\n",
       "      <td>5.651471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           seriesuid      coordX      coordY  \\\n",
       "0  1.3.6.1.4.1.14519.5.2.1.6279.6001.100225287222... -128.699421 -175.319272   \n",
       "\n",
       "       coordZ  diameter_mm  \n",
       "0 -298.387506     5.651471  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodules_df = pd.read_csv('/data/MRT/luna/CSVFILES/annotations.csv')\n",
    "nodules_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preprocessing = (Pipeline()\n",
    "                 .load(fmt='raw')\n",
    "                 .unify_spacing(shape=(92, 256, 256), spacing=(3.5, 1.0, 1.0))\n",
    "                 .fetch_nodules_info(nodules_df)\n",
    "                 .create_mask())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, real computation happens **only** when you pass a part of your `Dataset` - a `Batch` through the workflow: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch = (luna_dataset >> preprocessing).next_batch(batch_size=3, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numeric_ix</th>\n",
       "      <th>coordZ</th>\n",
       "      <th>coordY</th>\n",
       "      <th>coordX</th>\n",
       "      <th>diameter_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>164</td>\n",
       "      <td>207</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numeric_ix  coordZ  coordY  coordX  diameter_pixels\n",
       "0           1      32     137      77               10\n",
       "1           1      41     164     207                4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import get_nodules_pixel_coords\n",
    "get_nodules_pixel_coords(batch).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAGNCAYAAAA8dlRQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ce0559710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import show_slices\n",
    "show_slices(batch, 1, [32, 32], components=['images', 'masks'], clims=[(-1200, 300), (0, 1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first glance, nothing stops you now from training a segmenting net on full-sized scans. This, however, would require either a **very fast** CPU or a GPU with **a lot** of memory, as few full-sized scans can fit in, say, 8 GB of 1080 Ti-memory. Consider also the following: frequently, scans of Luna-dataset contain more than one cancerous nodule. What a waste to use a scan like that as only one training example! That being said, it may be a good idea to crop out **parts of scans** and train a net on these parts. Action `sample_nodules` allows you to do just that, as you will see in a minute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Sampling batches of crops with `sample_nodules`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loaded cancerous masks are necessary requirement for running `sample_nodules`. So, your pipeline should start with something like that:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preprocessing = (Pipeline()\n",
    "                 .load(fmt='raw')\n",
    "                 .unify_spacing(shape=(128, 256, 256), spacing=(3.5, 1.0, 1.0))\n",
    "                 .fetch_nodules_info(nodules_df)\n",
    "                 .create_mask())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say, you want to generate batches of **8** crops of shape **(16, 32, 32)**. You need to simply add `sample_nodules` to your `preprocessing`, specifying `batch_size` and `nodule_size`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crops_sampling_pipeline = preprocessing.sample_nodules(batch_size=8, nodule_size=(16, 32, 32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out the results by passing a batch of 5 scans through the workflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "batch_crops = (luna_dataset >> crops_sampling_pipeline).next_batch(5, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numeric_ix</th>\n",
       "      <th>coordZ</th>\n",
       "      <th>coordY</th>\n",
       "      <th>coordX</th>\n",
       "      <th>diameter_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numeric_ix  coordZ  coordY  coordX  diameter_pixels\n",
       "0           0       8      16      16               10"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_nodules_pixel_coords(batch_crops).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAGNCAYAAABQcAZVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAwCB/62nsKIqkDABgQMoAAAYCL6WqzoZtMyoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cefa089e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_slices(batch_crops, 0, 8, components=['images', 'masks'], clims=[(-1200, 300), (0, 1)], grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balancing batches: changing `share` of cancerous crops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that generated batch contains **8** crops: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(batch_crops)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still, how much of them contain cancer? Let's find this out by printing number of cancerous pixels in each crop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scan 0</th>\n",
       "      <th>Scan 1</th>\n",
       "      <th>Scan 2</th>\n",
       "      <th>Scan 3</th>\n",
       "      <th>Scan 4</th>\n",
       "      <th>Scan 5</th>\n",
       "      <th>Scan 6</th>\n",
       "      <th>Scan 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of cancerous  pixels:</th>\n",
       "      <td>676</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Scan 0  Scan 1  Scan 2  Scan 3  Scan 4  Scan 5  \\\n",
       "Number of cancerous  pixels:      676      50      25      50      72      72   \n",
       "\n",
       "                               Scan 6  Scan 7  \n",
       "Number of cancerous  pixels:        0       0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import num_of_cancerous_pixels\n",
    "num_of_cancerous_pixels(batch_crops)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, **six out of eight** crops contain cancerous pixels. The last two crops are simply cropped from random locations inside the scans. You may want to change the proportion and generate a balanced batch with **half (that is, four out of eight)** of crops being cancerous. For this you only need to set parameter `share` to **0.5**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crops_sampling_pipeline = preprocessing.sample_nodules(batch_size=8, nodule_size=(16, 32, 32),\n",
    "                                                       share=(0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_crops = (luna_dataset >> crops_sampling_pipeline).next_batch(5, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only **4** crops containing cancer in a generated batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scan 0</th>\n",
       "      <th>Scan 1</th>\n",
       "      <th>Scan 2</th>\n",
       "      <th>Scan 3</th>\n",
       "      <th>Scan 4</th>\n",
       "      <th>Scan 5</th>\n",
       "      <th>Scan 6</th>\n",
       "      <th>Scan 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of cancerous  pixels:</th>\n",
       "      <td>2646</td>\n",
       "      <td>50</td>\n",
       "      <td>72</td>\n",
       "      <td>676</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Scan 0  Scan 1  Scan 2  Scan 3  Scan 4  Scan 5  \\\n",
       "Number of cancerous  pixels:     2646      50      72     676       0       0   \n",
       "\n",
       "                               Scan 6  Scan 7  \n",
       "Number of cancerous  pixels:        0       0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_of_cancerous_pixels(batch_crops)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Importantly, generating batches of crops with `sample_nodules` involves setting **two different `batch_size`-parameters**. The first one appears directly in `sample_nodules` action and defines the *size of a resulting batch of crops*. In its turn, the second one is a parameter of `next_batch` and defines the number of scans flowing into the workflow:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![title](imgs/flow_with_sn.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "When setting parameters `share`, `batch_size` in `next_batch` and `batch_size` in `sample_nodules`, keep in mind that cancerous nodules occur with freqency **~1.3 nodules per scan** in Luna-dataset. That is, to generate batch of crops with **10** cancerous nodules, you should take not less than **8-10** (=10 / 1.3 + \"just-in-case\" margin) scans. E.g., with this combination of parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* `next_batch`: `batch_size` = 10\n",
    "*  `sample_nodules`: `share` = 0.6\n",
    "* `sample_nodules`: `batch_size` = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "you will get the expected **12 (=20 * 0.6)** cancerous nodules and **8** noncancerous for most batches. In the same time, `batch_size=5` in `next_batch` is unlikely to yield **12** cancerous crops. In this case, `sample_nodules` just takes all cancerous crops it can find, and complements them with random crops to form a batch of expected size (**20**, in our example).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building batches with only cancerous crops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may sometimes want to generate batches containing only (and all) cancerous crops, that can be found in a batch, flowing into a pipeline. For that you only have to set `batch_size` to `None` and `share` to `1.0`:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crops_sampling_pipeline = preprocessing.sample_nodules(batch_size=None, nodule_size=(16, 32, 32),\n",
    "                                                       share=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, however, you will end up getting batches of different sizes in different runs of `next_batch`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "batch_crops_1 = (luna_dataset >> crops_sampling_pipeline).next_batch(4)\n",
    "batch_crops_2 = (luna_dataset >> crops_sampling_pipeline).next_batch(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(batch_crops_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(batch_crops_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that all crops contain cancer now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scan 0</th>\n",
       "      <th>Scan 1</th>\n",
       "      <th>Scan 2</th>\n",
       "      <th>Scan 3</th>\n",
       "      <th>Scan 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of cancerous  pixels:</th>\n",
       "      <td>25</td>\n",
       "      <td>784</td>\n",
       "      <td>363</td>\n",
       "      <td>784</td>\n",
       "      <td>1620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Scan 0  Scan 1  Scan 2  Scan 3  Scan 4\n",
       "Number of cancerous  pixels:       25     784     363     784    1620"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_of_cancerous_pixels(batch_crops_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Randomizing locations of cancer in crops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look one more time at a cancerous crop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAGNCAYAAABQcAZVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ce01c7048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_slices(batch_crops, 2, 8, components=['images', 'masks'], clims=[(-1200, 300), (0, 1)],\n",
    "            grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that cancer is located in the **center** of the crop. This is the default behaviour of `sample_nodules`. To avoid possible overfitting, `you` can introduce variability in the location of cancer inside crops by supplying `variance`-parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crops_sampling_pipeline = preprocessing.sample_nodules(batch_size=None, nodule_size=(16, 32, 32),\n",
    "                                                       share=1.0, variance=(9, 36, 36))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `variance` should always be a sequence of three numbers. Each number is a variance of a zero-mean normal distribution used for sampling locations of cancer inside crops. Try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "batch_crops = (luna_dataset >> crops_sampling_pipeline).next_batch(5, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numeric_ix</th>\n",
       "      <th>coordZ</th>\n",
       "      <th>coordY</th>\n",
       "      <th>coordX</th>\n",
       "      <th>diameter_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numeric_ix  coordZ  coordY  coordX  diameter_pixels\n",
       "0           0       9       7      18                4\n",
       "1           1       4      22      25               16"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_nodules_pixel_coords(batch_crops).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAGNCAYAAABQcAZVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEcnA/wcBxwmyfaynXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ce011c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_slices(batch_crops, 1, 4, components=['images', 'masks'], clims=[(-1200, 300), (0, 1)],\n",
    "            grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic rule of thumb is to chose `variance` in a way, so that squared root of `variance` wouldn't exceed the quarter of size of crop (`nodule_size`) along respective axis (read about [$2\\sigma$](https://en.wikipedia.org/wiki/68–95–99.7_rule))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Shortcut to RadIO capabilities: `pipelines`-submodule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up to this point we've covered a lot of actions (`unify_spacing`, `resize`, `sample_nodules`, `rotate`,...) that offer flexible preprocessing capabilities. Still, chaining them in a correct way in a workflow can be a challenge. To make life easier for you, RadIO-team prepared a `pipelines`-submodule, incroporating several ready-to-use workflows. These are: `get_crops` - a simple workflow, preparing batches of crops for training a neural net, and workflows `split_dump` and `combine_crops`, that allow for **very fast** generation of training examples for neural networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### `get_crops`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can make a fresh crops-generating workflow in one line of code. Do not forget to supply `nodules`-dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from radio.pipelines import get_crops\n",
    "crops_sampling = get_crops(nodules_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most importantly, `workflows` produced by `get_crops` contain actions `unify_spacing` and `sample_nodules`. RadIO team set the arguments of these actions to good [default values](link-on-default). However, nothing stops you from setting your own parameters. E.g., you may want to generate batches of **6** cancerous and **4** noncancerous crops of shape **[24, 48, 48]**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crops_sampling = get_crops(nodules_df, share=0.6, batch_size=10, nodule_shape=(24, 48, 48))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out batches generated by this workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_crops = (luna_dataset >> crops_sampling).next_batch(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that **six out of ten** crops are indeed cancerous:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scan 0</th>\n",
       "      <th>Scan 1</th>\n",
       "      <th>Scan 2</th>\n",
       "      <th>Scan 3</th>\n",
       "      <th>Scan 4</th>\n",
       "      <th>Scan 5</th>\n",
       "      <th>Scan 6</th>\n",
       "      <th>Scan 7</th>\n",
       "      <th>Scan 8</th>\n",
       "      <th>Scan 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of cancerous  pixels:</th>\n",
       "      <td>405</td>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>5427</td>\n",
       "      <td>5193</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Scan 0  Scan 1  Scan 2  Scan 3  Scan 4  Scan 5  \\\n",
       "Number of cancerous  pixels:      405     864       0     144    5427    5193   \n",
       "\n",
       "                               Scan 6  Scan 7  Scan 8  Scan 9  \n",
       "Number of cancerous  pixels:        0       0       0       0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_of_cancerous_pixels(batch_crops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numeric_ix</th>\n",
       "      <th>coordZ</th>\n",
       "      <th>coordY</th>\n",
       "      <th>coordX</th>\n",
       "      <th>diameter_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numeric_ix  coordZ  coordY  coordX  diameter_pixels\n",
       "0           0       9      21      23                7"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_nodules_pixel_coords(batch_crops).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAGNCAYAAABQcAZVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6pSJiIiINAB1ykREREQawH/vLyyqL+AU/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ccd149ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_slices(batch_crops, 0, 9, components=['images', 'masks'], clims=[(0, 255), (0, 1)],\n",
    "            grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: we recommend to use `get_crops` only for testing purposes, as it generates no more than a couple of thousand of training examples in one pass through Luna-dataset (which takes several hours to complete). For production and research we encourage you to take a look at `split_dump` and `combine_crops`-workflows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating large datasets of crops with `split_dump`  and `combine_crops`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `split_dump` you can generate (and dump on disk) more than **100000** training examples in one run through the Luna-dataset. Workflow `combine_crops` can then be used for loading balanced batches of generated crops. You should always use one pipeline after another, starting with `split_dump`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1: `split_dump`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As opposed to `get_crops`-workflow, that uses batches of several scans for sampling batches of **10-30** crops, `split_dump` samples **thousands** crops from each batch, and dumps them on disk. As a result, one run through Luna-dataset is all it takes to generate **100000** training examples. Importantly, `split_dump` dumps cancerous and noncancerous crops in *separate folders*. Keep it in mind, when you use the method to produce a workflow. Also, do not forget to supply Luna-annotations dataframe: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from radio.pipelines import split_dump\n",
    "cancerous_folder, non_cancerous_folder = '/data/crops_folder/cancer', '/data/crops_folder/noncancer'\n",
    "crops_dumping = split_dump(cancerous_folder, non_cancerous_folder, nodules_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now `run` the whole dataset through the created workflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# (luna_dataset >> crops_dumping).run()\n",
    "\n",
    "# running the whole dataset Luna through the workflow takes several hours\n",
    "# so, think thoroughly before uncommenting the first line and running the cell!\n",
    "# (crops will also take about 100GB of your disk-space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At least for purposes of demonstration, it might be a better idea to `run` a small part of Luna through the workflow. Gladly, a `Dataset`-instance supports easy splitting on train-test-validation parts, just like that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "luna_dataset.split([0.8, 0.1, 0.1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You now have a train part of `Luna_dataset`, that has **80 %** of items from the whole Luna:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "710"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(luna_dataset.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "888"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(luna_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For purposes of demonstration, we suggest you to pass an even smaller part through the workflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "luna_dataset.split([0.03])\n",
    "len(luna_dataset.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<radio.dataset.dataset.pipeline.Pipeline at 0x7fb8600c1588>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# may take a couple of minutes\n",
    "(luna_dataset.train >> crops_dumping).run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should now be able to see a lot of directories in `cancerous_folder` and `noncancerous_folder`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2900"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(cancerous_folder))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4120"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(non_cancerous_folder))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are crops, dumped with `dump`-action. Read the [documentation](https://analysiscenter.github.io/lung_cancer/intro/preprocessing.html#load-and-dump) to understand the content of folders and what `dump` does."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2: `combine_crops`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`combine-crops` allows you create batches, balancing cancerous and noncancerous crops in any way you want. The workflow always works with crops created by `split_dump`. So, the first step is to associate datasets with folders, created in previous section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cancer_index = FilesIndex(path=os.path.join(cancerous_folder, '*'), dirs=True)\n",
    "non_cancer_index = FilesIndex(path=os.path.join(non_cancerous_folder, '*'), dirs=True)\n",
    "cancer_set = Dataset(cancer_index, batch_class=CTImagesMaskedBatch)\n",
    "non_cancer_set = Dataset(non_cancer_index, batch_class=CTImagesMaskedBatch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it, you can create a fresh workflow using `combine_crops`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from radio.pipelines import combine_crops\n",
    "crops_sampling = combine_crops(cancer_set, non_cancer_set, batch_sizes=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pay attention to parameter `batch_sizes`. In the example above, it is set to **(5, 5)**. This means, that each batch generated by crops_sampling, will contain **5** cancerous and **5** noncancerous crops. See it yourself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "batch_crops = crops_sampling.next_batch()\n",
    "# wow! This works really fast!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scan 0</th>\n",
       "      <th>Scan 1</th>\n",
       "      <th>Scan 2</th>\n",
       "      <th>Scan 3</th>\n",
       "      <th>Scan 4</th>\n",
       "      <th>Scan 5</th>\n",
       "      <th>Scan 6</th>\n",
       "      <th>Scan 7</th>\n",
       "      <th>Scan 8</th>\n",
       "      <th>Scan 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of cancerous  pixels:</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>601</td>\n",
       "      <td>405</td>\n",
       "      <td>486</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Scan 0  Scan 1  Scan 2  Scan 3  Scan 4  Scan 5  \\\n",
       "Number of cancerous  pixels:        0       0       0       0       0     196   \n",
       "\n",
       "                               Scan 6  Scan 7  Scan 8  Scan 9  \n",
       "Number of cancerous  pixels:      601     405     486      75  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_of_cancerous_pixels(batch_crops)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may want to generate batches containing **8** cancerous crops and **2** noncancerous:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "crops_sampling = combine_crops(cancer_set, non_cancer_set, batch_sizes=(8, 2))\n",
    "batch_crops = crops_sampling.next_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scan 0</th>\n",
       "      <th>Scan 1</th>\n",
       "      <th>Scan 2</th>\n",
       "      <th>Scan 3</th>\n",
       "      <th>Scan 4</th>\n",
       "      <th>Scan 5</th>\n",
       "      <th>Scan 6</th>\n",
       "      <th>Scan 7</th>\n",
       "      <th>Scan 8</th>\n",
       "      <th>Scan 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of cancerous  pixels:</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>601</td>\n",
       "      <td>405</td>\n",
       "      <td>486</td>\n",
       "      <td>75</td>\n",
       "      <td>200</td>\n",
       "      <td>196</td>\n",
       "      <td>1568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Scan 0  Scan 1  Scan 2  Scan 3  Scan 4  Scan 5  \\\n",
       "Number of cancerous  pixels:        0       0     196     601     405     486   \n",
       "\n",
       "                               Scan 6  Scan 7  Scan 8  Scan 9  \n",
       "Number of cancerous  pixels:       75     200     196    1568  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_of_cancerous_pixels(batch_crops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAGNCAYAAABQcAZVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP5QbvBxPkV1lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ccd183588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_slices(batch_crops, 9, 10, components=['images', 'masks'], clims=[(0, 255), (0, 1)],\n",
    "            grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### What's next?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reading this tutorial you should be able to generate batches of crops with any type of balance using `sample_nodules`. You also should be able to use predefined workflow `split_dump` to **dump a large dataset of crops** on disk and then load balanced batches of cancerous/noncancerous crops in the blink of an eye with `combine_crops`.\n",
    "\n",
    "In the [4th](https://github.com/analysiscenter/radio/blob/master/tutorials/RadIO.IV.ipynb) (and last) tutorial you will be able to make use of all these crops and train a **segmenting [VNet](https://arxiv.org/abs/1606.04797)-like model on task of cancer-segmentation**."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
